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Annotation 7669287881996

Conda tips:

Install all the packages that you need in the new environment at the same time. Installing packages one at a time can lead to dependency conflicts.

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Recommendations for Avoiding Dependency Conflicts with Conda There are two simple rules to follow: Always create a new environment for each project Install all the packages that you need in the new environment at the same time. Installing packages one at a time can lead to dependency conflicts. To create an environment with a specific version of Python and multiple packages including a package with a specific version: $ conda create -n <env_name> python=<version#>

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How to Manage Python Dependencies with Conda - ActiveState
rmine the Current Environment with Conda The current or active environment is shown in parentheses () or brackets [] at the beginning of the Anaconda Prompt or terminal: (<current_env>) $ <span>Recommendations for Avoiding Dependency Conflicts with Conda There are two simple rules to follow: Always create a new environment for each project Install all the packages that you need in the new environment at the same time. Installing packages one at a time can lead to dependency conflicts. To create an environment with a specific version of Python and multiple packages including a package with a specific version: $ conda create -n <env_name> python=<version#> <packagename> <packagename> <packagename>=<version#> Alternatively, you can use conda to install all the packages in a requirements.txt file. You can save a requirements.txt file from an existing environment, or manually create a new requirements.txt for a different environment. To create a conda requirements.txt file from an existing environment: Activate your project environment. See section above entitled “How to Activate an Environment with Conda” for detai

Annotation 7707095862540

#abm #agent-based #machine-learning #model #priority

We demonstrated the advantages of this approach by applying it to reproduce the results of the prominent Sugarscape model. To show the flexibility of the framework, we then made slight changes to the modelled system by removing the competition between the agents. While a traditional approach to agent-based modelling would require a reformulation of the rules for agent behaviour, here the Neural Network is automatically retrained to accommodate the changes in the system and we naturally end up with realistic agent behaviour

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negative or neutral. Here, the Neural Network is not used as a form of optimization, but rather as a realistic depiction of a decision process, including the possibility of errors in judgement. <span>We demonstrated the advantages of this approach by applying it to reproduce the results of the prominent Sugarscape model. To show the flexibility of the framework, we then made slight changes to the modelled system by removing the competition between the agents. While a traditional approach to agent-based modelling would require a reformulation of the rules for agent behaviour, here the Neural Network is automati- cally retrained to accommodate the changes in the system and we naturally end up with realistic agent behaviour. We also explored the limits of the framework and found that the original approach fails, once system states that are relevant, if agents act to reach a goal, do not appear during an Ex

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Annotation 7708323220748

#RNN #ariadne #behaviour #consumer #deep-learning #priority #recurrent-neural-networks #retail #simulation #synthetic-data

In the context of behaviour prediction, we want to understand how previous consumer actions influence model predictions:

- How does order probability change when products are put into the cart?

- Does it decrease significantly if a consumer does not return to a webshop for two days?

Answers to these questions are consumer-specific; they depend on the complete consumer history

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As machine learning models become ubiquitous in our everyday lives, demand for explaining their predictions is growing [5, 16, 14]. In the context of behaviour prediction, we want to understand how previous consumer actions influence model predictions: How does order probability change when products are put into the cart? Does it decrease significantly if a consumer does not return to a webshop for two days? Answers to these questions are consumer-specific; they depend on the complete consumer history

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Annotation 7766497168652

#causal #inference

Inverse probability matching (a.k.a propensity score matching) is not technically a G-method. While it shares the fundamental goal of addressing confounding and selection bias, it employs a different mathematical framework and estimation approach

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The G-methods family, as developed by James Robins and colleagues, specifically includes: Inverse probability weighting (IPW) G-computation G-estimation of structural nested models Inverse probability matching (propensity score matching) is not technically a G-method. While it shares the fundamental goal of addressing confounding and selection bias, it employs a different mathematical framework and estimation approach

Flashcard 7788706008332

Tags

#recurrent-neural-networks #rnn

Question

customer defection in non-contractual business settings is by definition unobserved by the firm and thus needs to be [...] inferred from past transaction

Answer

indirectly

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customer defection in non-contractual business settings is by definition unobserved by the firm and thus needs to be indirectly inferred from past transaction

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Annotation 7788708629772

#deep-learning #keras #lstm #python #sequence

Stacked LSTMs are now a stable technique for challenging sequence prediction problems

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they found that the depth of the network was more important than the number of memory cells in a given layer to model skill. Stacked LSTMs are now a stable technique for challenging sequence prediction problems. A Stacked LSTM architecture can be defined as an LSTM model comprised of multiple LSTM layers

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Flashcard 7788710726924

Tags

#ggplot2

Question

The problem here is that by default scales::percent() multiplies its input value by 100. This can be controlled by the [...] parameter.

Answer

scale

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The problem here is that by default scales::percent() multiplies its input value by 100. This can be controlled by the scale parameter.

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Something is not right here! 4000%!? That seems a bit excessive. The problem here is that by default scales::percent() multiplies its input value by 100. This can be controlled by the scale parameter. scales::percent(100, scale = 1) Copy ## [1] "100%" However, scale_y_continuous() expects a function as input for its labels parameter not the actual labels itself. Thus, using percent() is not an option anymore. Fortu

Annotation 7788712561932

#recurrent-neural-networks #rnn

In subscription-based or contractual settings customer ‘‘churn” events are directly observable,

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Contrary to subscription-based or contractual settings where customer ‘‘churn” events are directly observable, customer defection in non-contractual business settings is by definition unobserved by the firm and thus needs to be indirectly inferred from past transaction behavior (Reinartz & K

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Flashcard 7788717542668

Tags

#causal #inference

Question

Inverse probability matching (propensity score matching) is not technically a [...]. While it shares the fundamental goal of addressing confounding and selection bias, it employs a different mathematical framework and estimation approach

Answer

G-method

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Inverse probability matching (propensity score matching) is not technically a G-method. While it shares the fundamental goal of addressing confounding and selection bias, it employs a different mathematical framework and estimation approach

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Flashcard 7788719115532

Tags

#causal #inference

Question

Inverse probability [...] is not technically a G-method. While it shares the fundamental goal of addressing confounding and selection bias, it employs a different mathematical framework and estimation approach

Answer

matching

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Flashcard 7788721736972

Tags

#causal #inference

Question

Inverse probability matching (a.k.a [...] matching) is not technically a G-method. While it shares the fundamental goal of addressing confounding and selection bias, it employs a different mathematical framework and estimation approach

Answer

propensity score

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Inverse probability matching (a.k.a propensity score matching) is not technically a G-method. While it shares the fundamental goal of addressing confounding and selection bias, it employs a different mathematical framework and estimation

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Flashcard 7788724620556

Tags

#deep-learning #keras #lstm #python #sequence

Question

Unfortunately, the range of contextual information that standard [...]s can access is in practice quite limited. The problem is that the influence of a given input on the hidden layer, and therefore on the network output, either decays or blows up exponentially as it cycles around the network’s recurrent connections.

Answer

RNN

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Unfortunately, the range of contextual information that standard RNNs can access is in practice quite limited. The problem is that the influence of a given input on the hidden layer, and therefore on the network output, either decays or blows up exponent

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Flashcard 7788726455564

Tags

#recurrent-neural-networks #rnn

Question

Toth, Tan, Di Fabbrizio, and Datta (2017) have shown that a mixture of RNNs can approximate several complex functions [...].

Answer

simultaneously

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Toth, Tan, Di Fabbrizio, and Datta (2017) have shown that a mixture of RNNs can approximate several complex functions simultaneously.

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on 20-Jan-2026 (Tue)

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